code: def test_stable_diffusion_upscale_batch(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      unet = self.dummy_cond_unet_upscale
      low_res_scheduler = DDPMScheduler()
      scheduler = DDIMScheduler(prediction_type="v_prediction")
      vae = self.dummy_vae
      text_encoder = self.dummy_text_encoder
      tokenizer = CLIPTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-clip"
      )
  
      image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
      low_res_image = (
          Image.fromarray(np.uint8(image))
          .convert("RGB")
          .resize((64, 64))
      )
  
      # make sure here that pndm scheduler skips prk
      sd_pipe = StableDiffusionUpscalePipeline(
          unet=unet,
          low_res_scheduler=low_res_scheduler,
          scheduler=scheduler,
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          max_noise_level=350,
      )
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger"
      output = sd_pipe(
          2 * [prompt],
          image=2 * [low_res_image],
          guidance_scale=6.0,
          noise_level=20,
          num_inference_steps=2,
          output_type="np",
      )
      image = output.images
      assert image.shape[0] == 2
  
      generator = torch.Generator(device=device).manual_seed(0)
      output = sd_pipe(
          [prompt],
          image=low_res_image,
          generator=generator,
          num_images_per_prompt=2,
          guidance_scale=6.0,
          noise_level=20,
          num_inference_steps=2,
          output_type="np",
      )
      image = output.images
      assert image.shape[0] == 2
  
imports: import gc
  import random
  import tempfile
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 7 for calling the StableDiffusionUpscalePipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_upscale_batch() without return types